The Default Mode Network in a Hierarchical Generative Model of the Brain

We embed the default mode network (DMN) within a predictive processing architecture, arguing that its core function is to coordinate high-level predictions in the brain’s hierarchical generative model. Rather than mapping DMN regions onto broad psychological categories such as memory, social cognition, or emotion, we propose that the network plays a unified computational role. At a global level, the DMN occupies a privileged position in large-scale cortical organization, supporting abstract, multimodal representations that drive predictions across extended spatial and temporal scales. At a local level, this role is structured by systematic functional-anatomical variation within the network itself. We describe three broad axes of DMN organization grounded in computational neuroanatomy. First, a longitudinal axis differentiates a posterior complex that is more tightly coupled to high-dimensional sensory systems from an anterior complex with stronger connectivity to visceromotor subcortical structures and longer integration timescales. This axis reflects graded differences in predictive scope rather than a simple external–internal divide. Second, hierarchical gradients of a generative architecture support increasing abstraction, not only between the DMN and other networks, but also within DMN complexes. Third, patterned laminar connectivity enables bidirectional predictive signaling between DMN complexes. Together, this framework links the DMN’s global computational role to its internal structural organization and provides a mechanistic account of its role in large-scale brain function.